Overview

Dataset statistics

Number of variables12
Number of observations83140
Missing cells0
Missing cells (%)0.0%
Duplicate rows11902
Duplicate rows (%)14.3%
Total size in memory7.6 MiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical2

Alerts

Dataset has 11902 (14.3%) duplicate rowsDuplicates
avgOdds is highly overall correlated with catOdds and 1 other fieldsHigh correlation
zscore is highly overall correlated with catOdds and 1 other fieldsHigh correlation
catOdds is highly overall correlated with avgOdds and 2 other fieldsHigh correlation
catOddsNUm is highly overall correlated with avgOdds and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-04-01 13:07:42.988426
Analysis finished2023-04-01 13:07:58.228986
Duration15.24 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

avgOdds
Real number (ℝ)

Distinct1398
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462099
Minimum1.042
Maximum7.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:07:58.312005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.042
5-th percentile1.254
Q11.668
median2.08
Q32.614
95-th percentile4.646
Maximum7.4
Range6.358
Interquartile range (IQR)0.946

Descriptive statistics

Standard deviation1.0613052
Coefficient of variation (CV)0.4523488
Kurtosis4.6477419
Mean2.3462099
Median Absolute Deviation (MAD)0.458
Skewness1.9806352
Sum195063.89
Variance1.1263688
MonotonicityNot monotonic
2023-04-01T09:07:58.431031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.08 583
 
0.7%
2.15 563
 
0.7%
2.09 545
 
0.7%
2.03 534
 
0.6%
2.06 533
 
0.6%
2.04 526
 
0.6%
2.05 521
 
0.6%
2.14 519
 
0.6%
2.16 519
 
0.6%
2.11 500
 
0.6%
Other values (1388) 77797
93.6%
ValueCountFrequency (%)
1.042 12
 
< 0.1%
1.044 6
 
< 0.1%
1.048 6
 
< 0.1%
1.05 6
 
< 0.1%
1.052 12
 
< 0.1%
1.054 12
 
< 0.1%
1.056 12
 
< 0.1%
1.058 6
 
< 0.1%
1.06 42
0.1%
1.062 6
 
< 0.1%
ValueCountFrequency (%)
7.4 45
0.1%
7.38 2
 
< 0.1%
7.36 13
 
< 0.1%
7.35 21
< 0.1%
7.33 6
 
< 0.1%
7.32 6
 
< 0.1%
7.3 24
< 0.1%
7.25 18
 
< 0.1%
7.22 6
 
< 0.1%
7.21 8
 
< 0.1%

buildUpPlaySpeed
Real number (ℝ)

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.774898
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:07:58.560061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q146
median55
Q364
95-th percentile70
Maximum80
Range60
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.714186
Coefficient of variation (CV)0.21783743
Kurtosis-0.39697987
Mean53.774898
Median Absolute Deviation (MAD)9
Skewness-0.37943638
Sum4470845
Variance137.22215
MonotonicityNot monotonic
2023-04-01T09:07:58.683100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 5260
 
6.3%
60 4660
 
5.6%
50 4601
 
5.5%
45 4154
 
5.0%
70 4001
 
4.8%
49 3946
 
4.7%
48 3422
 
4.1%
55 3401
 
4.1%
64 3121
 
3.8%
35 2798
 
3.4%
Other values (46) 43776
52.7%
ValueCountFrequency (%)
20 432
 
0.5%
23 19
 
< 0.1%
24 389
 
0.5%
25 292
 
0.4%
26 245
 
0.3%
28 107
 
0.1%
29 223
 
0.3%
30 1684
2.0%
31 270
 
0.3%
32 151
 
0.2%
ValueCountFrequency (%)
80 18
 
< 0.1%
78 279
 
0.3%
77 130
 
0.2%
76 362
 
0.4%
75 833
 
1.0%
74 134
 
0.2%
73 388
 
0.5%
72 421
 
0.5%
71 680
 
0.8%
70 4001
4.8%

buildUpPlayPassing
Real number (ℝ)

Distinct58
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.558383
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:07:58.821120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q138
median48
Q355
95-th percentile68
Maximum80
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.412091
Coefficient of variation (CV)0.2399596
Kurtosis-0.52154763
Mean47.558383
Median Absolute Deviation (MAD)8
Skewness0.20495447
Sum3954004
Variance130.23582
MonotonicityNot monotonic
2023-04-01T09:07:58.944136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 7725
 
9.3%
35 5217
 
6.3%
30 4608
 
5.5%
40 4087
 
4.9%
55 3870
 
4.7%
52 3769
 
4.5%
45 3229
 
3.9%
48 2760
 
3.3%
65 2724
 
3.3%
38 2708
 
3.3%
Other values (48) 42443
51.1%
ValueCountFrequency (%)
20 150
 
0.2%
22 136
 
0.2%
23 291
 
0.4%
24 245
 
0.3%
25 152
 
0.2%
26 412
 
0.5%
27 19
 
< 0.1%
28 136
 
0.2%
29 586
 
0.7%
30 4608
5.5%
ValueCountFrequency (%)
80 146
 
0.2%
79 38
 
< 0.1%
77 16
 
< 0.1%
75 184
 
0.2%
74 35
 
< 0.1%
73 547
 
0.7%
72 338
 
0.4%
71 100
 
0.1%
70 2343
2.8%
69 296
 
0.4%

chanceCreationPassing
Real number (ℝ)

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.888393
Minimum21
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:07:59.073164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q146
median52
Q362
95-th percentile70
Maximum80
Range59
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.263269
Coefficient of variation (CV)0.21296296
Kurtosis-0.6670847
Mean52.888393
Median Absolute Deviation (MAD)8
Skewness-0.11366402
Sum4397141
Variance126.86123
MonotonicityNot monotonic
2023-04-01T09:07:59.189192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50 5963
 
7.2%
49 5420
 
6.5%
52 4471
 
5.4%
65 4188
 
5.0%
70 4146
 
5.0%
55 4123
 
5.0%
48 3582
 
4.3%
68 3418
 
4.1%
53 3081
 
3.7%
60 2802
 
3.4%
Other values (39) 41946
50.5%
ValueCountFrequency (%)
21 136
 
0.2%
28 304
 
0.4%
30 2229
2.7%
31 130
 
0.2%
32 400
 
0.5%
33 124
 
0.1%
34 896
 
1.1%
35 2632
3.2%
36 751
 
0.9%
37 1402
1.7%
ValueCountFrequency (%)
80 151
 
0.2%
77 360
 
0.4%
76 145
 
0.2%
73 218
 
0.3%
72 946
 
1.1%
71 584
 
0.7%
70 4146
5.0%
69 1317
 
1.6%
68 3418
4.1%
67 1344
 
1.6%

chanceCreationCrossing
Real number (ℝ)

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.390149
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:07:59.326222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q148
median54
Q364
95-th percentile70
Maximum80
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.507277
Coefficient of variation (CV)0.21156913
Kurtosis-0.40552142
Mean54.390149
Median Absolute Deviation (MAD)9
Skewness-0.30082601
Sum4521997
Variance132.41742
MonotonicityNot monotonic
2023-04-01T09:07:59.452254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 5153
 
6.2%
50 5128
 
6.2%
52 4896
 
5.9%
60 4725
 
5.7%
65 4667
 
5.6%
54 3892
 
4.7%
55 3227
 
3.9%
51 2992
 
3.6%
45 2765
 
3.3%
53 2519
 
3.0%
Other values (45) 43176
51.9%
ValueCountFrequency (%)
20 169
 
0.2%
23 37
 
< 0.1%
24 152
 
0.2%
25 190
 
0.2%
26 270
 
0.3%
27 37
 
< 0.1%
30 1220
1.5%
31 725
 
0.9%
33 302
 
0.4%
34 1946
2.3%
ValueCountFrequency (%)
80 300
 
0.4%
78 446
 
0.5%
77 317
 
0.4%
76 357
 
0.4%
75 148
 
0.2%
74 166
 
0.2%
73 771
 
0.9%
72 1178
 
1.4%
71 378
 
0.5%
70 5153
6.2%

chanceCreationShooting
Real number (ℝ)

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.597558
Minimum22
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:07:59.575282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile35
Q149
median54
Q364
95-th percentile70
Maximum80
Range58
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.101351
Coefficient of variation (CV)0.20333054
Kurtosis-0.36386384
Mean54.597558
Median Absolute Deviation (MAD)7
Skewness-0.22287525
Sum4539241
Variance123.23999
MonotonicityNot monotonic
2023-04-01T09:07:59.695309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 7601
 
9.1%
70 6953
 
8.4%
55 5184
 
6.2%
52 4621
 
5.6%
65 4230
 
5.1%
53 3085
 
3.7%
60 2990
 
3.6%
64 2926
 
3.5%
49 2723
 
3.3%
56 2682
 
3.2%
Other values (44) 40145
48.3%
ValueCountFrequency (%)
22 136
 
0.2%
23 298
 
0.4%
24 152
 
0.2%
29 259
 
0.3%
30 477
 
0.6%
31 151
 
0.2%
32 412
 
0.5%
33 129
 
0.2%
34 918
1.1%
35 1913
2.3%
ValueCountFrequency (%)
80 604
 
0.7%
79 152
 
0.2%
78 131
 
0.2%
77 37
 
< 0.1%
76 134
 
0.2%
75 340
 
0.4%
73 533
 
0.6%
72 1090
 
1.3%
71 250
 
0.3%
70 6953
8.4%

defencePressure
Real number (ℝ)

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.721301
Minimum23
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:07:59.825339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile30
Q139
median46
Q354
95-th percentile66
Maximum72
Range49
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.481106
Coefficient of variation (CV)0.2243325
Kurtosis-0.31198233
Mean46.721301
Median Absolute Deviation (MAD)7
Skewness0.316323
Sum3884409
Variance109.85359
MonotonicityNot monotonic
2023-04-01T09:07:59.957369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
45 7233
 
8.7%
35 4929
 
5.9%
47 4377
 
5.3%
50 3912
 
4.7%
30 3824
 
4.6%
70 3565
 
4.3%
55 3358
 
4.0%
43 3110
 
3.7%
49 3051
 
3.7%
40 2974
 
3.6%
Other values (38) 42807
51.5%
ValueCountFrequency (%)
23 449
 
0.5%
24 51
 
0.1%
25 448
 
0.5%
26 307
 
0.4%
27 167
 
0.2%
28 232
 
0.3%
29 261
 
0.3%
30 3824
4.6%
31 295
 
0.4%
32 179
 
0.2%
ValueCountFrequency (%)
72 136
 
0.2%
70 3565
4.3%
68 302
 
0.4%
67 152
 
0.2%
66 436
 
0.5%
65 1442
1.7%
64 843
 
1.0%
63 850
 
1.0%
62 252
 
0.3%
61 889
 
1.1%

defenceAggression
Real number (ℝ)

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.109538
Minimum27
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:08:00.083397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile34
Q144
median49
Q356
95-th percentile70
Maximum72
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.7218657
Coefficient of variation (CV)0.19401228
Kurtosis-0.30729301
Mean50.109538
Median Absolute Deviation (MAD)6
Skewness0.22565969
Sum4166107
Variance94.514673
MonotonicityNot monotonic
2023-04-01T09:08:00.197293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
45 8107
 
9.8%
55 5102
 
6.1%
70 4826
 
5.8%
50 4706
 
5.7%
47 4623
 
5.6%
44 3613
 
4.3%
48 3329
 
4.0%
60 3309
 
4.0%
52 2846
 
3.4%
40 2815
 
3.4%
Other values (36) 39864
47.9%
ValueCountFrequency (%)
27 52
 
0.1%
28 112
 
0.1%
29 16
 
< 0.1%
30 2537
3.1%
31 148
 
0.2%
32 149
 
0.2%
33 304
 
0.4%
34 1217
1.5%
35 1276
1.5%
36 16
 
< 0.1%
ValueCountFrequency (%)
72 136
 
0.2%
71 134
 
0.2%
70 4826
5.8%
69 134
 
0.2%
68 220
 
0.3%
67 843
 
1.0%
66 425
 
0.5%
65 2519
3.0%
64 184
 
0.2%
63 811
 
1.0%

defenceTeamWidth
Real number (ℝ)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.416406
Minimum29
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:08:00.313319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile35
Q148
median51
Q358
95-th percentile70
Maximum73
Range44
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.3075371
Coefficient of variation (CV)0.17756916
Kurtosis-0.21656165
Mean52.416406
Median Absolute Deviation (MAD)5
Skewness-0.061253671
Sum4357900
Variance86.630246
MonotonicityNot monotonic
2023-04-01T09:08:00.420354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
50 9021
 
10.9%
65 5361
 
6.4%
51 4756
 
5.7%
55 4637
 
5.6%
49 4367
 
5.3%
70 4070
 
4.9%
53 3612
 
4.3%
52 3599
 
4.3%
54 3304
 
4.0%
45 3198
 
3.8%
Other values (32) 37215
44.8%
ValueCountFrequency (%)
29 98
 
0.1%
30 1590
1.9%
32 170
 
0.2%
33 68
 
0.1%
34 315
 
0.4%
35 2319
2.8%
36 526
 
0.6%
37 432
 
0.5%
38 1000
1.2%
39 1181
1.4%
ValueCountFrequency (%)
73 131
 
0.2%
70 4070
4.9%
69 444
 
0.5%
68 1546
 
1.9%
67 693
 
0.8%
66 568
 
0.7%
65 5361
6.4%
64 893
 
1.1%
63 571
 
0.7%
62 757
 
0.9%

zscore
Real number (ℝ)

Distinct1398
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49633663
Minimum0.000552804
Maximum2.9992498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.7 KiB
2023-04-01T09:08:00.535380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000552804
5-th percentile0.044992004
Q10.21363089
median0.41168702
Q30.65418235
95-th percentile1.3042444
Maximum2.9992498
Range2.998697
Interquartile range (IQR)0.44055146

Descriptive statistics

Standard deviation0.43896147
Coefficient of variation (CV)0.88440274
Kurtosis8.3207554
Mean0.49633663
Median Absolute Deviation (MAD)0.21664557
Skewness2.4574671
Sum41265.427
Variance0.19268717
MonotonicityNot monotonic
2023-04-01T09:08:00.657397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.275052592 583
 
0.7%
0.231969666 563
 
0.7%
0.268897889 545
 
0.7%
0.305826111 534
 
0.6%
0.287362 533
 
0.6%
0.299671408 526
 
0.6%
0.293516704 521
 
0.6%
0.23812437 519
 
0.6%
0.225814962 519
 
0.6%
0.256588481 500
 
0.6%
Other values (1388) 77797
93.6%
ValueCountFrequency (%)
0.000552804 26
 
< 0.1%
0.000678137 23
 
< 0.1%
0.001783745 54
 
0.1%
0.001909077 175
0.2%
0.004245626 165
0.2%
0.004370959 88
0.1%
0.005476567 15
 
< 0.1%
0.0056019 28
 
< 0.1%
0.006707508 30
 
< 0.1%
0.00683284 36
 
< 0.1%
ValueCountFrequency (%)
2.999249816 45
0.1%
2.986940408 2
 
< 0.1%
2.974631001 13
 
< 0.1%
2.968476297 21
< 0.1%
2.956166889 6
 
< 0.1%
2.950012185 6
 
< 0.1%
2.937702778 24
< 0.1%
2.906929259 18
 
< 0.1%
2.888465148 6
 
< 0.1%
2.882310444 8
 
< 0.1%

catOdds
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size649.7 KiB
Likely win
21441 
Almost Guaranteed Win
21347 
Unlikely win
21117 
Likley loss
19235 

Length

Max length21
Median length12
Mean length13.5637
Min length10

Characters and Unicode

Total characters1127686
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlmost Guaranteed Win
2nd rowAlmost Guaranteed Win
3rd rowAlmost Guaranteed Win
4th rowAlmost Guaranteed Win
5th rowAlmost Guaranteed Win

Common Values

ValueCountFrequency (%)
Likely win 21441
25.8%
Almost Guaranteed Win 21347
25.7%
Unlikely win 21117
25.4%
Likley loss 19235
23.1%

Length

2023-04-01T09:08:00.773423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T09:08:00.881366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
win 63905
34.1%
likely 21441
 
11.4%
almost 21347
 
11.4%
guaranteed 21347
 
11.4%
unlikely 21117
 
11.3%
likley 19235
 
10.3%
loss 19235
 
10.3%

Most occurring characters

ValueCountFrequency (%)
i 125698
11.1%
l 123492
11.0%
n 106369
 
9.4%
e 104487
 
9.3%
104487
 
9.3%
k 61793
 
5.5%
y 61793
 
5.5%
s 59817
 
5.3%
a 42694
 
3.8%
t 42694
 
3.8%
Other values (11) 294362
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 897365
79.6%
Uppercase Letter 125834
 
11.2%
Space Separator 104487
 
9.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 125698
14.0%
l 123492
13.8%
n 106369
11.9%
e 104487
11.6%
k 61793
6.9%
y 61793
6.9%
s 59817
6.7%
a 42694
 
4.8%
t 42694
 
4.8%
w 42558
 
4.7%
Other values (5) 125970
14.0%
Uppercase Letter
ValueCountFrequency (%)
L 40676
32.3%
W 21347
17.0%
G 21347
17.0%
A 21347
17.0%
U 21117
16.8%
Space Separator
ValueCountFrequency (%)
104487
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1023199
90.7%
Common 104487
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 125698
12.3%
l 123492
12.1%
n 106369
10.4%
e 104487
10.2%
k 61793
 
6.0%
y 61793
 
6.0%
s 59817
 
5.8%
a 42694
 
4.2%
t 42694
 
4.2%
w 42558
 
4.2%
Other values (10) 251804
24.6%
Common
ValueCountFrequency (%)
104487
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1127686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 125698
11.1%
l 123492
11.0%
n 106369
 
9.4%
e 104487
 
9.3%
104487
 
9.3%
k 61793
 
5.5%
y 61793
 
5.5%
s 59817
 
5.3%
a 42694
 
3.8%
t 42694
 
3.8%
Other values (11) 294362
26.1%

catOddsNUm
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size649.7 KiB
2
21441 
1
21347 
3
21117 
4
19235 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 21441
25.8%
1 21347
25.7%
3 21117
25.4%
4 19235
23.1%

Length

2023-04-01T09:08:00.979167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T09:08:01.082690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 21441
25.8%
1 21347
25.7%
3 21117
25.4%
4 19235
23.1%

Most occurring characters

ValueCountFrequency (%)
2 21441
25.8%
1 21347
25.7%
3 21117
25.4%
4 19235
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 21441
25.8%
1 21347
25.7%
3 21117
25.4%
4 19235
23.1%

Most occurring scripts

ValueCountFrequency (%)
Common 83140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 21441
25.8%
1 21347
25.7%
3 21117
25.4%
4 19235
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 21441
25.8%
1 21347
25.7%
3 21117
25.4%
4 19235
23.1%

Interactions

2023-04-01T09:07:56.171459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:44.260703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.520771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.838780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.166669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:49.511269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.068040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:52.391209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.640849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.901786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:56.303451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:44.384237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.662390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.974098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.305700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:49.642309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.202071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:52.512239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.765877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.024814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:56.435481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:44.511268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.791337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:47.110191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.437725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:49.773328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.331111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:52.636296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.889905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.163778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:56.574512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:44.636300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.921377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:47.245459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.573161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:49.906367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.463173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:52.767340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.021936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.290798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:56.708543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:44.762330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.051912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:47.378525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.707206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:50.035403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.596409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:52.905363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.152807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.419793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:56.843762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:44.888572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.191371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:47.513521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.840237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:50.176419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.734279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.034814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.279836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.548807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:56.973348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.019525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.324401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:47.641550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.980271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:50.305040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.862309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.157854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.408189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.677314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:57.100377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.140871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.449610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:47.770579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:49.114301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:50.428896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:51.995338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.276767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.530777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.805376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:57.225793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.263844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.573493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:47.898608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:49.253210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:50.548923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:52.129479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.400795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.652586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:55.929406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:57.351822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:45.389778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:46.700332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:48.037639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:49.375239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:50.939011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:52.255174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:53.517822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:54.777781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T09:07:56.048431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-01T09:08:01.177733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
avgOddsbuildUpPlaySpeedbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidthzscorecatOddscatOddsNUm
avgOdds1.0000.0380.182-0.0440.011-0.132-0.180-0.059-0.069-0.3170.8270.827
buildUpPlaySpeed0.0381.0000.2870.2710.1870.152-0.0070.127-0.070-0.0130.0800.080
buildUpPlayPassing0.1820.2871.0000.1900.238-0.095-0.1750.047-0.056-0.0950.1220.122
chanceCreationPassing-0.0440.2710.1901.0000.2430.1480.1900.1590.0370.0160.0960.096
chanceCreationCrossing0.0110.1870.2380.2431.0000.0100.0380.0790.042-0.0200.0690.069
chanceCreationShooting-0.1320.152-0.0950.1480.0101.0000.1760.0990.1460.0830.1270.127
defencePressure-0.180-0.007-0.1750.1900.0380.1761.0000.2560.3830.0930.1290.129
defenceAggression-0.0590.1270.0470.1590.0790.0990.2561.0000.0550.0380.0670.067
defenceTeamWidth-0.069-0.070-0.0560.0370.0420.1460.3830.0551.0000.0430.0820.082
zscore-0.317-0.013-0.0950.016-0.0200.0830.0930.0380.0431.0000.6710.671
catOdds0.8270.0800.1220.0960.0690.1270.1290.0670.0820.6711.0001.000
catOddsNUm0.8270.0800.1220.0960.0690.1270.1290.0670.0820.6711.0001.000

Missing values

2023-04-01T09:07:57.506857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-01T09:07:58.015931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

avgOddsbuildUpPlaySpeedbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidthzscorecatOddscatOddsNUm
01.27670454570654050400.769891Almost Guaranteed Win1
11.27665406565704545650.769891Almost Guaranteed Win1
21.27646544660554050560.769891Almost Guaranteed Win1
31.27646384668374949560.769891Almost Guaranteed Win1
41.27646544972564241560.769891Almost Guaranteed Win1
51.27638444944405453560.769891Almost Guaranteed Win1
61.20070454570654050400.816667Almost Guaranteed Win1
71.20065406565704545650.816667Almost Guaranteed Win1
81.20046544660554050560.816667Almost Guaranteed Win1
91.20046384668374949560.816667Almost Guaranteed Win1
avgOddsbuildUpPlaySpeedbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidthzscorecatOddscatOddsNUm
831302.0748445342535339570.281207Likely win2
831312.0748445342535339570.281207Likely win2
831322.0748445342535339570.281207Likely win2
831332.0748445342535339570.281207Likely win2
831343.4330304540703545450.555832Likley loss4
831353.4354566761594856670.555832Likley loss4
831363.4348445342535339570.555832Likley loss4
831373.4348445342535339570.555832Likley loss4
831383.4348445342535339570.555832Likley loss4
831393.4348445342535339570.555832Likley loss4

Duplicate rows

Most frequently occurring

avgOddsbuildUpPlaySpeedbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidthzscorecatOddscatOddsNUm# duplicates
14821.36249524954674647470.716960Almost Guaranteed Win124
32841.63264414952644644510.550783Almost Guaranteed Win112
38421.74064414952644644510.484313Likely win212
56542.01049495253674346500.318136Likely win212
60412.04050445260403740450.299671Likely win212
61432.05049495253674346500.293517Likely win212
65412.08049535234363645490.275053Likely win212
85692.27049495253674346500.158113Unlikely win312
89722.33664414952644644510.117492Unlikely win312
211.06035323731356163650.902832Almost Guaranteed Win110